CN114362133B - Power grid stability control method under homogenization condition - Google Patents
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Abstract
The invention relates to the technical field of power distribution network bearing capacity assessment, in particular to a power grid stability control method under homogenization conditions, which comprises the following steps: 1) Based on the configuration rule of the minimum number PMUs in the information network, adopting a PMU optimization method for avoiding repeated schemes to define a model; 2) Obtaining a topology inequality of the power network; 3) Describing the transient stability problem of the intelligent power grid as a control task with consistent centralization and speed of a plurality of intelligent agent groups, and describing each intelligent agent according to a second-order dynamics model of the plurality of intelligent agents; 4) Defining a Lyapunov function as the total energy of the system; 5) And solving the stability control of the power grid. Compared with the prior art, the stability control method based on the linearization variable parameter tracking method can effectively identify weak links affecting the running stability of the power grid, provides a basis for the stability control of the alternating current-direct current series-parallel system containing large-scale new energy, improves the stability and reliability of the system, and enhances the running capability of the intelligent power grid.
Description
Technical Field
The invention relates to the technical field of power grid safety and stability control, in particular to a power grid stability control method under homogenization conditions.
Background
The extra-high voltage alternating current/direct current transmission can realize large-scale, long-distance and large-capacity energy transmission and cross-region asynchronous networking. With the increase of the proportion of the extra-high voltage alternating current/direct current power grid in the whole power system, the influence of the direct current power grid on the whole power transmission system is researched, and the realization of the rapid evaluation and weak link discrimination of the reliability of the whole alternating current/direct current system is particularly important. In recent years, researchers have gradually increased the attention of safety and stability assessment of ultra-high voltage power grids. At present, a stability evaluation model of a double-pulse ultra-high voltage direct current transmission system exists, but the influence of an alternating current system is not fully considered. Meanwhile, stability evaluation of the power network of all voltage levels under the extra-high voltage main grid is researched, but a relatively comprehensive model is not established for an extra-high voltage alternating current-direct current system in the aspect of reliability evaluation indexes. Up to the present, the reliability assessment of the extra-high voltage alternating current-direct current hybrid system does not have a mature and comprehensive index system and assessment method.
From the perspective of energy homogenization, in the process of converting kinetic energy in an alternating current-direct current series-parallel system into transient potential energy, more transient energy can be born by a power transmission element on a cutting set, so that a tearing phenomenon is generated. The cut set is the weakest link of the alternating current-direct current series-parallel system under a certain fault. The transient energy of the system is excessively concentrated after the fault, so that the alternating current-direct current series-parallel system is unstable, and the transient energy variation on the branch and the cut set can be used as a criterion for identifying the system instability. Information networks and information flows will play an important role in enhancing the operational capabilities of smart grids.
In actual operation, the problems of excessive dependence of a system on information, overhead of communication and information processing and the like need to be considered: including situations where redundant information and less relevant information are present and the computational burden is excessive resulting in poor performance. One of the strategies to improve the flexibility of smart grids is to determine the proper degree of dependence on the information network and the information flow. The information measuring equipment PMU is reasonably configured in quantity and position, and the overall objective of observability, economy and reliability is achieved. The invention introduces the optimized configuration of PMU in the reliability improvement of the information network to determine the information dependency degree, which is a necessary means for the transient state stable real-time control of the system under the information-physical fusion network.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a power grid stability control method under a homogenization condition, which has the following technical scheme:
the power grid stability control method under the homogenization condition comprises the following steps:
1) Based on the configuration rule of the minimum number PMUs in the information network, adopting a PMU optimization method for avoiding repeated schemes to define a model;
2) Obtaining a topology inequality of the power network;
3) Describing the transient stability problem of the intelligent power grid as a control task with consistent centralization and speed of a plurality of intelligent agent groups, and describing each intelligent agent according to a second-order dynamics model of the plurality of intelligent agents;
4) Defining a Lyapunov function as the total energy of the system;
5) A power grid stability control solving method.
Defining a model in the step 1), wherein the expression is as follows:
Where x k is 1 or 0, indicating whether a physical grid node k has PMU equipment installed.
The power network topology inequality obtained in step 2) is expressed as follows:
Where G represents the set of all nodes connected to node k.
The power network topology inequality is obtained in the step 2), and the steps are as follows:
listing a node association matrix, wherein the expression is as follows:
wherein a= (a ij) is a node association matrix, two adjacent or equal nodes of i and j are a ij =1, otherwise a ij =0; 1 is a column vector with elements all 1;
The cost of installing PMU devices at node i is expressed as follows:
yi＝1+0.1nch (1-4)
according to the constraint rules, the power network topology is fully and observably available, and the expression is as follows:
Wherein, f k＝xk+∑xl;
To further illustrate the effect of the number of channels, the following variables are introduced for illustration, the expression:
The above formula can then be rewritten to obtain the power network topology inequality.
In step 3), each agent is described as follows:
In the step 3), each agent is described, and the steps are as follows:
Forward calculation, namely directly calculating a standard shape correction equation of the alternating current system by adopting a sparse matrix solving algorithm, and solving to obtain a voltage correction quantity DeltaU (k) and a voltage correction quantity DeltaU (k) of the kth iteration Updating the k+1 step variable of the alternating current system, wherein the expression is as follows:
U(k+1)＝U(k)+ΔU(k) (1-8)
Back-generation calculation, the voltage correction obtained by forward calculation Substituting the standard shape correction equation of the direct current system, combining the transfer terms, and directly calculating the coordination variable correction amount/>, of the kth iterationAnd DC variable modifier/>And updating a k+1 step variable of the direct current system, wherein the expression is as follows:
All variables in the system are obtained until the unbalance amount delta D (k) of the direct current system and the unbalance amount delta F (k) of the alternating current system meet a convergence criterion, and the iteration is finished;
Defining a controller signal u i＝PL,j, wherein the intelligent body i=1, … and F correspond to the intelligent body containing the dominant generator, and as each partition only selects one dominant generator, F simultaneously represents the number of the partitions of the physical power grid; h=diag [ h 1…,hi+1,hi+1…hZ ], when i is generally equal to or less than F, h=1, otherwise h=0; the corresponding V= { F+i, …, Z } of other auxiliary regulating agents in the same partition, the auxiliary regulating power of the corresponding generator is P F+i＝aF+i·PL,i, a represents the element of which the scaling factor L changing along with the power of the dominant generator is a physical relation matrix L, and the expression is as follows:
At this point a new control amount u i is introduced defining the controller signal u i as:
Wherein, element B i≥(100*Di in the information relation matrix B);
Let M i＝Di+Bi determine a second order system in the set containing the dominant generator agent, expressed as follows:
In order to realize transient stability of the smart grid, a distributed control signal is applied to an agent containing a dominant generator in each partition, and the expression is as follows:
Considering a second-order system consisting of expected values of guide feedback items in a kinetic equation, assuming that initial speeds are not matched, and the initial energy H 0 is a limited value, under the action of a control protocol, all agents are asymptotically converged to corresponding speed reference values in a consistent manner, and corresponding to the expected values of the rotating speeds of the generators, finally realizing global stable group consistent behaviors, wherein the expression is as follows:
Fjk＝gjkEj[Ej-Ekcos(θj-θk)]-bjkEjEksin(θj-θk) (1-18)
Pi＝Pi0-KGi(f-f0) (1-19)
Wherein P ej、Qej respectively represents the active power and the reactive power of the node j; e j、Ek each represents the voltage at node jk; g jk、bjk represents the conductance and susceptance, respectively, on the line card; k Gi represents the active power frequency static characteristic coefficient of the ith generator; f represents a system frequency value; p i0、f0 represents the generator active power and system frequency at this point in the operating state.
In step 4), the Lyapunov function is defined as the total energy of the system, namely the total potential energy between the intelligent bodies and the sum of the relative potential energy and the kinetic energy between the physical quantity of the intelligent bodies and the expected reference quantity, and the expression is as follows:
In step 4), defining the Lyapunov function as the total energy of the system, and the steps are as follows:
Since the potential energy function V is symmetrical, the expression is as follows:
the derivation is available, and the expression is as follows:
The expression is as follows:
since L is a semi-positive Laplace matrix and c >0, L+cI is a positive matrix and therefore H <0, the agents are asymptotically stable to their respective desired reference under control input.
In the step 5), the power grid stability control solution is carried out, and the steps are as follows:
Step 5.1) screening and classifying faults, namely rapidly screening out unstable faults by using the quantization capability of EEAC and identifying UM thereof, and classifying all the unstable faults into all same UM fault subsets by taking UM as a characteristic;
Step 5.2) respectively executing the preventive control and emergency control coordinated optimization of the stability constraint of each same UM fault subset;
Step 5.3) analyzing whether stability control conflict exists among sub-optimizations according to the topological relation of each fault subset UM and a specific control scene;
step 5.4) coordinating the sub-optimizations until convergence conditions are met, as shown in fig. 1, and finally, for safety reasons, fault screening can be performed on the system to ensure that the system is not subject to potential dangerous accidents after control optimization.
Compared with the prior art, the beneficial effects of the method are as follows:
The invention provides a stable control method based on a linearization variable parameter tracking method, which can effectively identify weak links affecting the running stability of a power grid, provides a basis for the stability control of an alternating current-direct current series-parallel system containing large-scale new energy, improves the stability and reliability of the system, and enhances the running capability of the intelligent power grid.
Drawings
FIG. 1 is a flow chart of a power grid stability control solution;
FIG. 2 is a graph of relative rotor angle versus time for each generator under the fault condition;
FIG. 3 is a graph of rotational speed of each generator over time under a fault condition;
FIG. 4 is a graph of relative rotor angle of each generator over time using a centralized control method;
FIG. 5 is a graph of rotational speed of each generator over time using a centralized control method;
FIG. 6 is a graph of relative rotor angle of each generator over time using a bee congestion control method;
FIG. 7 shows the rotational speed of each generator over time using a bee congestion control method;
fig. 8 shows the motion trajectories of the respective agents in the information space.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
The following describes some embodiments of the present invention with reference to fig. 1-8.
An IEEE39 node smart grid model was subjected to an example analysis, which includes 19 loads and 46 lines, and simulation study was performed using a MATLAB/Simulink platform. The examples are divided into four scenarios according to the classification of short-circuit faults, centralized control after faults and beehive control after faults.
Example 1
Scene 1: assuming that a three-phase short circuit fault occurs on the power grid disturbed line 21-22 when t=0 s, the fault line is disconnected when t=0.1 s, and the controller for connecting the information and the physical network is not activated, so that the information network traffic and the calculated amount are minimum. The relation between the rotation speed and the rotor angle of each generator is shown in fig. 2 and 3, the system is obviously unstable in operation, the rotor angle has a tendency that the difference value is continuously enlarged, and the power network has serious faults.
Example 2
Scene 2: in the centralized control, each node of the power grid transmits information to a dispatching center, receives a control instruction of the center after centralized processing, and needs a large amount of PMU equipment and communication lines to ensure information acquisition and transmission. The collection of the whole power grid state information is required to be completed in a period before centralized control is performed after fault removal, and the collection and processing of data can be completed in a preset critical time after fault processing under ideal conditions. At t=0.15 s, all generator nodes are subjected to deadbeat control. The ideal conditions of the generator rotor angle and the rotating speed which change with time are obtained as shown in fig. 4 and 5, and the system reaches a stable state in a short time. But the system is in a maximum information transfer and processing state. The control equipment of each generator node frequently utilizes an external energy device to carry out power adjustment on the corresponding node, the overall energy consumption of the whole adjustment process is large, and the performance of a network topological structure and related equipment is not fully utilized.
Example 3
Scene 3: the stability performance of the adoption of the bee congestion control method is shown in fig. 6 and 7, the distributed bee congestion control can reduce the equipment cost, improve the conditions of large centralized control information processing capacity and large energy consumption, and is in a critical stability range in a 5s observation period, but has a larger upper and lower interval range, and the stability time of each intelligent power grid after the intelligent power grid intelligent agent is controlled is longer.
Example 4
Scene 4: the control performance using the method of the present invention is shown in fig. 8. The smart grid partition identification scheme should be started in a preset critical time point after fault processing so as to avoid larger damage conditions and improve the capability of the smart grid for restoring transient stability. The partition optimization control is applied to the intelligent agent containing the dominant generator, the partition of the intelligent power grid is firstly determined, the intelligent power grid is divided according to the similarity of physical quantities describing the dynamic characteristics of the intelligent agents, the physical quantity change of the intelligent agents in the intelligent power grid corresponds to the motion trend of the individual in the information space, and the motion trail of the individual carrying the state information is described as an individual motion curve in the two-dimensional space as shown in fig. 8. From the trend of the separation among the individuals in the information space, namely the change of the physical quantity of each intelligent agent after the fault, the G1 individual is obviously far away from other individuals, the movement trend of G6 and G7 is closest, and the dynamic identification partitioning scheme of each intelligent agent is obtained.
Example 5
As shown in fig. 1, the power grid stability control solution flow is as follows:
Step a, a system ground state;
Screening and classifying faults in the step b;
C, respectively executing constraint prevention control and emergency control coordination optimization of each through UM fault set;
Step d, judging whether stability control conflicts exist among all accident constraints, if not, ending, and outputting a stability control scheme; if yes, continuing to execute the step e;
Step e, analyzing conflict reasons according to decoupling iteration, aggregation coordination and a coordination method according to a specific scene;
Step f, entering a global coordination optimizing algorithm.
And secondly, on the basis of information network reliability optimization, by combining the actual active power of each intelligent agent and the sensitivity weight arrangement of the intelligent agent to node data, reasonable dominant generators G1, G7 and G9 are respectively selected for each partition, and distributed optimization control is applied to the intelligent agent containing the dominant generators so as to realize the aim of transient stability of the system. And finishing partition identification and dominant generator selection within critical time after fault removal, and applying a distributed control scheme combining information-physical network data to the intelligent power grid when t=0.15 s. As shown in fig. 8, a time-dependent curve of the state quantity of each agent is obtained, each agent in each partition achieves a control target, and the corresponding physical quantity converges to a certain value in each partition. Because the external energy injection can influence the actual operation to cause certain fluctuation, the observation shows that the power system is quickly restored to a stable operation state, the rotation speed of the generator is stable and asymptotically consistent, the rotor angle meets constraint requirements and tends to be in a stable state, good effects are obtained under the conditions of small information use and external energy injection, the system restoration and stabilization time is shorter, the upper and lower interval ranges of physical state quantity are smaller, and the stability margin of the system is increased.
It will be appreciated by those skilled in the art that embodiments of the application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (1)
1. The power grid stability control method under the homogenization condition is characterized by comprising the following steps of:
1) Based on the configuration rule of the minimum number PMUs in the information network, adopting a PMU optimization method for avoiding repeated schemes to define a model;
the definition model has the following expression:
wherein x k is 1 or 0, which indicates whether the physical power grid node k is provided with PMU equipment;
2) Obtaining a power network topology inequality based on the definition model;
The obtained power network topology inequality has the following expression:
Wherein G represents all node sets connected with the node k; the power network topology inequality is obtained in the step 2), and the steps are as follows: listing a node association matrix, wherein the expression is as follows:
Wherein a= (a ij) is a node association matrix, two adjacent or equal nodes of i and j are a ij =1, otherwise a ij =0; 1 is a column vector with elements all 1;
The cost of installing PMU devices at node i is expressed as follows:
yi＝1+0.1nch (1-4)
according to the constraint rules, the power network topology is fully and observably available, and the expression is as follows:
Wherein, f k＝xk+∑xl;
To further illustrate the effect of the number of channels, the following variables are introduced for illustration, the expression:
then the above-described available power network topology inequality is rewritten;
3) Describing the transient stability problem of the intelligent power grid as a control task with consistent centralization and speed of a plurality of intelligent agent groups, and describing each intelligent agent according to a second-order dynamics model of the plurality of intelligent agents;
the expression describing each agent is as follows:
forward calculation, namely directly calculating a standard shape correction equation of the alternating current system by adopting a sparse matrix solving algorithm, and solving to obtain a voltage correction quantity DeltaU (k) and a voltage correction quantity DeltaU (k) of the kth iteration Updating the k+1 step variable of the alternating current system, wherein the expression is as follows:
U(k+1)＝U(k)+ΔU(k) (1-8)
Back-generation calculation, the voltage correction obtained by forward calculation Substituting the standard shape correction equation of the direct current system, combining the transfer terms, and directly calculating the coordination variable correction amount/>, of the kth iterationAnd DC variable modifier/>Updating the k+1th step variable of the direct current system, wherein the expression is as follows:
All variables in the system are obtained until the unbalance amount delta D (k) of the direct current system and the unbalance amount delta F (k) of the alternating current system meet a convergence criterion, and the iteration is finished;
Defining a controller signal u i＝PL,j, wherein the intelligent body i=1, … and F correspond to the intelligent body containing the dominant generator, and as each partition only selects one dominant generator, F simultaneously represents the number of the partitions of the physical power grid; h=diag [ h 1…,hi+1,hi+1…hZ ], when i is generally equal to or less than F, h=1, otherwise h=0; the corresponding V= { F+i, …, Z } of other auxiliary regulating agents in the same partition, the auxiliary regulating power of the corresponding generator is P F+i＝aF+i·PL,i, a represents the element of which the scaling factor L changing along with the power of the dominant generator is a physical relation matrix L, and the expression is as follows:
At this point a new control amount u i is introduced defining the controller signal u i as:
Wherein, element B i≥(100*Di in the information relation matrix B);
Let M i＝Di+Bi determine a second order system in the set containing the dominant generator agent, expressed as follows:
In order to realize transient stability of the smart grid, a distributed control signal is applied to an agent containing a dominant generator in each partition, and the expression is as follows:
Considering a second-order system consisting of expected values of guide feedback items in a kinetic equation, assuming that initial speeds are not matched, and the initial energy H 0 is a limited value, under the action of a control protocol, all agents are asymptotically converged to corresponding speed reference values, and corresponding to the expected values of the rotating speeds of the generators, finally realizing global stable group consistency behavior, wherein the expression is as follows:
Fjk＝gjkEj[Ej-Ekcos(θj-θk)]-bjkEjEksin(θj-θk) (1-18)
Pi＝Pi0-KGi(f-f0) (1-19)
Wherein P ej、Qej respectively represents the active power and the reactive power of the node j; e j、Ek each represents the voltage at node jk; g jk、bjk represents the conductance and susceptance, respectively, on the line card; k Gi represents the active power frequency static characteristic coefficient of the ith generator; f represents a system frequency value; p i0、f0 represents the generator active power and system frequency in the current operating state;
4) Defining a Lyapunov function as the total energy of the system;
the Lyapunov function is defined as the total energy of the system, namely the total potential energy between the intelligent agents and the sum of the relative potential energy and the kinetic energy between the physical quantity of the intelligent agents and the expected reference quantity, and the expression is as follows:
Since the potential energy function V is symmetrical, the expression is as follows:
the derivation is available, and the expression is as follows:
The expression is as follows:
since L is a semi-positive Laplace matrix and c >0, L+cI is a positive matrix, and therefore H <0, the agents are asymptotically stable to their respective desired reference under control input; 5) Solving the stability control of the power grid; the power grid stability control solution comprises the following steps:
Step 5.1) screening and classifying faults, rapidly screening out unstable faults and identifying UM thereof by using the quantization capability of EEAC,
Classifying all unstable faults into all same UM fault subsets by taking UM as a characteristic;
Step 5.2) respectively executing the preventive control and emergency control coordinated optimization of the stability constraint of each same UM fault subset;
Step 5.3) analyzing whether stability control conflict exists among sub-optimizations according to the topological relation of each fault subset UM and a specific control scene;
step 5.4) coordinates the individual sub-optimizations until a convergence condition is met.
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